Data Engineering with DuckDB & MotherDuck

Learn how to build data workflows that run on your laptop, in the cloud, or across both using DuckDB and MotherDuck.

Description

In this hands-on course, you’ll start by exploring DuckDB locally: querying CSV and Parquet files, building persistent databases, and analyzing data right from your terminal or the built-in DuckDB UI. You’ll then connect to MotherDuck, the cloud platform built around DuckDB, and learn how to scale analytics, share data, and collaborate without switching tools.

You’ll build hands-on ELT workflows using the DuckDB CLI, Python, and MotherDuck. From analyzing local CSV files to running cloud-scale data pipelines. You’ll see how hybrid execution works, compare local versus cloud compute, and learn to move effortlessly between environments while maintaining a single, simple toolset.

Along the way, you’ll work on a real-world project analyzing NYC 311 elevator service requests, combining local and cloud datasets to generate insights, visualize hotspots, and identify business opportunities.

Finally, you’ll explore DuckLake, DuckDB’s new integrated lakehouse format, which adds schema evolution, snapshots, and transactions to your Parquet data. You’ll understand where Duck Lake fits into the modern data stack and how it connects to cloud storage like S3.

By the end of this course, you’ll have a complete setup, reusable SQL and Python scripts, and the confidence to use DuckDB and MotherDuck together for modern, scalable data engineering workflows.

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